White balance adjusting method and system and computer equipment

Abstract

The invention provides a white balance adjustment method and system and computer equipment, and the method comprises the steps: equally dividing an original image into a plurality of regions, and determining a corresponding correction point in each region; all color channels of the correction points are converted into a YCrCb space, accumulated values of Cb components and Cr components corresponding to all the correction points are calculated based on constraint conditions, gain values of the color channels of the correction points are calculated based on the accumulated values of the Cb components and the Cr components, gain adjustment is conducted on all the correction points based on the gain values, and the color channels of all the correction points are obtained. Obtaining a target correction point; the pixel points in the areas approach the corresponding target correction points to perform white balance adjustment, and the white balance adjustment rate can be improved through the white balance adjustment method and the white balance adjustment device.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a white balance adjustment method, system and computer equipment. Background Art

[0002] White balance adjustment, also known as color temperature compensation, refers to using white as the base color in the image and restoring other colors in the image so that the colors in the restored image are the colors in the real world seen by the human eye, thereby achieving color restoration of the image.

[0003] Currently, white balance adjustment can cause changes in the brightness of color points in an image. In some optical design scenarios, there are requirements for the color coordinates and brightness of color points. If the brightness of a color point after white balance adjustment is insufficient, the color point does not meet the design requirements. The design range of color points is large, and for each color point within the design range, it is necessary to perform design proofing and test the brightness after white balance adjustment one by one to determine the color point within the color point range that meets the brightness requirements. This requires multiple trials and errors to obtain the color point that meets the brightness requirements of the white balance adjustment, which is inefficient. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a method to solve the deficiencies in the prior art.

[0005] To achieve the above object, the present invention provides a white balance adjustment method, the method comprising:

[0006] Dividing the original image into a plurality of regions, and determining corresponding correction points in each of the regions;

[0007] Converting each color channel of the correction point to a YCrCb space, and calculating the cumulative values ​​of the Cb component and the Cr component corresponding to each correction point based on the constraint conditions, calculating the gain value of the color channel of the correction point based on the cumulative values ​​of the Cb component and the Cr component, and performing gain adjustment on each correction point based on the gain value to obtain a target correction point;

[0008] The pixels in each of the areas are moved closer to the corresponding target correction point to perform white balance adjustment.

[0009] The beneficial effects of the present invention are as follows: by dividing the original image into multiple regions, and determining the corresponding correction point in each region, the color channel of the correction point is converted to the YCrCb space, and then the cumulative value of the Cb component and the Cr component of each color channel is calculated based on the constraint conditions, and then the gain value of each color channel in the correction point is calculated based on the cumulative value of the Cb component and the Cr component, and the correction point is gain-adjusted by the gain value to obtain the target correction point in each region, and then the pixel points in the region are moved closer to the corresponding target correction point to perform white balance adjustment, thereby improving the efficiency of white balance adjustment of the entire original image.

[0010] Furthermore, the method further comprises:

[0011] A YCbCr color coordinate system is established, with the intersection of two adjacent edges on the original image as the origin, the two edges starting from the origin as the Cb axis and the Cr axis respectively, and the plane passing through the origin and perpendicular to the Cb axis and the Cr axis as the Y axis.

[0012] Furthermore, the conversion expressions related to the step of converting each color channel of the correction point into the YCrCb space are as follows:

[0013]

[0014] Here, R, G, and B are component values ​​of different channels of the correction point, and Y, Cb, and Cr are the Y axis, Cb axis, and Cr axis of the YCbCr color coordinate system, respectively.

[0015] Furthermore, the step of calculating the gain value of the color channel of the correction point based on the accumulated value of the Cb component and the Cr component includes:

[0016] Comparing the accumulated value of the Cb component and the accumulated value of the Cr component with corresponding preset thresholds, respectively, and calculating the mean value of the Cb component and the mean value of the Cr component based on the corresponding comparison results;

[0017] The gain value of the color channel of the correction point is calculated based on the average value of the Cb component and the average value of the Cr component.

[0018] Furthermore, the expression for performing gain adjustment on each correction point based on the gain value is as follows:

[0019] r_o=r_i+k*mul_r

[0020] g_o=g_i

[0021] b_o=b_i+k*mul_b

[0022] Among them, r_o, g_o and b_o are the output values ​​of the R, G and B color channels of the correction point respectively, r_i, g_i and b_i are the values ​​of the color channels of the previous frame respectively, k is the rate factor, mul_r and mul_b are the gain values ​​of the G channel and B channel respectively.

[0023] Furthermore, before the step of moving the pixel points in each of the regions toward the corresponding target correction point, the method further includes:

[0024] The position of each pixel on the original image is obtained through the CL5 lens, and the area to which each pixel belongs is determined based on the position of the pixel.

[0025] In order to achieve the above object, the present invention further provides a white balance adjustment system for implementing the above white balance adjustment method, the system comprising:

[0026] A division module, configured to divide the original image into a plurality of regions and determine a corresponding correction point in each region;

[0027] an adjustment module, configured to convert each color channel of the correction point into a YCrCb space, and calculate, based on a constraint condition, a cumulative value of a Cb component and a Cr component corresponding to each correction point, respectively, calculate a gain value of the color channel of the correction point based on the cumulative value of the Cb component and the Cr component, and perform gain adjustment on each correction point based on the gain value to obtain a target correction point;

[0028] The moving-together module is used to move the pixel points in each of the areas toward the corresponding target correction point to perform white balance adjustment.

[0029] Furthermore, the system further comprises:

[0030] A YCbCr color coordinate system is established, with the intersection of two adjacent edges on the original image as the origin, the two edges starting from the origin as the Cb axis and the Cr axis respectively, and the plane passing through the origin and perpendicular to the Cb axis and the Cr axis as the Y axis.

[0031] Furthermore, the conversion expressions related to the step of converting each color channel of the correction point into the YCrCb space are as follows:

[0032]

[0033] Here, R, G, and B are component values ​​of different channels of the correction point, and Y, Cb, and Cr are the Y axis, Cb axis, and Cr axis of the YCbCr color coordinate system, respectively.

[0034] To achieve the above objectives, the present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the white balance adjustment method described above when executing the computer program.

[0035] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of a white balance adjustment method provided by the first embodiment of the present invention;

[0037] Figure 2 This is a structural block diagram of a white balance adjustment system provided by the second embodiment of the present invention.

[0038] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0040] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0041] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0042] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0043] Example 1

[0044] See also Figure 1 , which is a flow chart of the white balance adjustment method in the first embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0045] Step S101, dividing the original image into a plurality of regions, and determining a corresponding correction point in each region;

[0046] The original image is divided into 9 areas, and corresponding correction points are determined in each area.

[0047] Step S102: Convert each color channel of the correction point to a YCrCb space, calculate the cumulative value of the Cb component and the Cr component corresponding to each correction point based on the constraint condition, calculate the gain value of the color channel of the correction point based on the cumulative value of the Cb component and the Cr component, and perform gain adjustment on each correction point based on the gain value to obtain a target correction point;

[0048] The color channel of the pixel is first converted to the YCrCb space, the influence of brightness can be ignored, and the constraint condition is that a1*w b +b3<w r <a1*w b+b1,a2*w b +b4<w r <a2*w b +b2, where a1 and a2 are the offset slopes of the correction points at high and low color temperatures, respectively, and w b is the cumulative value of the Cb components of all the correction points, w r is the accumulated value of the Cr components of all the correction points, and b1, b2, b3, and b4 are fixed values.

[0049] Furthermore, the step of calculating the gain value of the color channel of the correction point based on the accumulated value of the Cb component and the Cr component includes:

[0050] Comparing the accumulated value of the Cb component and the accumulated value of the Cr component with corresponding preset thresholds, respectively, and calculating the mean value of the Cb component and the mean value of the Cr component based on the corresponding comparison results;

[0051] The gain value of the color channel of the correction point is calculated based on the average value of the Cb component and the average value of the Cr component.

[0052] The accumulated values ​​of the Cb component and the Cr component are accumulated in multiple steps to perform gain adjustment to obtain corresponding gain values, and only the R and B channels are adjusted, while the G channel remains unchanged.

[0053] Furthermore, the expression for performing gain adjustment on each correction point based on the gain value is as follows:

[0054] r_o=r_i+k*mul_r

[0055] g_o=g_i

[0056] b_o=b_i+k*mul_b

[0057] Among them, r_o, g_o and b_o are the output values ​​of the R, G and B color channels of the correction point respectively, r_i, g_i and b_i are the values ​​of the color channels of the previous frame respectively, k is the rate factor, mul_r and mul_b are the gain values ​​of the G channel and B channel respectively.

[0058] In step S103 , the pixels in each of the regions are moved closer to the corresponding target correction point to perform white balance adjustment.

[0059] Through the above steps, the original image is divided into multiple regions, and a corresponding correction point is determined in each region. The color channel of the correction point is converted to the YCrCb space, and then the cumulative value of the Cb component and the Cr component of each color channel is calculated based on the constraint conditions. The gain value of each color channel in the correction point is then calculated based on the cumulative value of the Cb component and the Cr component. The correction point is gain-adjusted according to the gain value to obtain a target correction point in each region. Then, the pixel points in the region are moved closer to the corresponding target correction point to perform white balance adjustment, thereby improving the efficiency of white balance adjustment of the entire original image.

[0060] Furthermore, the method further comprises:

[0061] A YCbCr color coordinate system is established, with the intersection of two adjacent edges on the original image as the origin, the two edges starting from the origin as the Cb axis and the Cr axis respectively, and the plane passing through the origin and perpendicular to the Cb axis and the Cr axis as the Y axis.

[0062] Furthermore, the conversion expressions related to the step of converting each color channel of the correction point into the YCrCb space are as follows:

[0063]

[0064] Here, R, G, and B are component values ​​of different channels of the correction point, and Y, Cb, and Cr are the Y axis, Cb axis, and Cr axis of the YCbCr color coordinate system, respectively.

[0065] Furthermore, before the step of moving the pixel points in each of the regions toward the corresponding target correction point, the method further includes:

[0066] The position of each pixel on the original image is obtained through the CL5 lens, and the area to which each pixel belongs is determined based on the position of the pixel.

[0067] Example 2

[0068] See also Figure 2 , is a flowchart of a white balance adjustment system in a second embodiment of the present invention, which is used to implement the white balance adjustment method in the first embodiment. The system includes:

[0069] A division module, configured to divide the original image into a plurality of regions and determine a corresponding correction point in each region;

[0070] an adjustment module, configured to convert each color channel of the correction point into a YCrCb space, and calculate, based on a constraint condition, a cumulative value of a Cb component and a Cr component corresponding to each correction point, respectively, calculate a gain value of the color channel of the correction point based on the cumulative value of the Cb component and the Cr component, and perform gain adjustment on each correction point based on the gain value to obtain a target correction point;

[0071] The moving-together module is used to move the pixel points in each of the areas toward the corresponding target correction point to perform white balance adjustment.

[0072] In specific implementation, the original image is divided into multiple areas, and the corresponding correction points are determined in each area. The color channels of the correction points are converted to YCrCb space, and then the cumulative values ​​of the Cb component and Cr component of each color channel are calculated based on the constraint conditions. The gain value of each color channel in the correction point is calculated based on the cumulative value of the Cb component and Cr component. The correction point is gain-adjusted according to the gain value to obtain the target correction point in each area. Then, the pixel points in the area are moved closer to the corresponding target correction point to perform white balance adjustment, thereby improving the efficiency of white balance adjustment of the entire original image.

[0073] Furthermore, the system also includes:

[0074] A YCbCr color coordinate system is established, with the intersection of two adjacent edges on the original image as the origin, the two edges starting from the origin as the Cb axis and the Cr axis respectively, and the plane passing through the origin and perpendicular to the Cb axis and the Cr axis as the Y axis.

[0075] Furthermore, the conversion expressions related to the step of converting each color channel of the correction point into the YCrCb space are as follows:

[0076]

[0077] Here, R, G, and B are component values ​​of different channels of the correction point, and Y, Cb, and Cr are the Y axis, Cb axis, and Cr axis of the YCbCr color coordinate system, respectively.

[0078] Furthermore, the adjustment module is specifically used to:

[0079] Comparing the accumulated value of the Cb component and the accumulated value of the Cr component with corresponding preset thresholds, respectively, and calculating the mean value of the Cb component and the mean value of the Cr component based on the corresponding comparison results;

[0080] The gain value of the color channel of the correction point is calculated based on the average value of the Cb component and the average value of the Cr component.

[0081] Furthermore, the adjustment module is specifically used to:

[0082] The expression for performing gain adjustment on each correction point based on the gain value is as follows:

[0083] r_o=r_i+k*mul_r

[0084] g_o=g_i

[0085] b_o=b_i+k*mul_b

[0086] Among them, r_o, g_o and b_o are the output values ​​of the R, G and B color channels of the correction point respectively, r_i, g_i and b_i are the values ​​of the color channels of the previous frame respectively, k is the rate factor, mul_r and mul_b are the gain values ​​of the G channel and B channel respectively.

[0087] Furthermore, the system further includes an acquisition module, which is specifically configured to:

[0088] The position of each pixel on the original image is obtained through the CL5 lens, and the area to which each pixel belongs is determined based on the position of the pixel.

[0089] Example 3

[0090] The third embodiment of the present invention is based on the same inventive concept and proposes a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the balance adjustment method described in the first embodiment is implemented.

[0091] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer device for use with, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer device" can be any device that includes storage, communication, propagation, or transmission of a program for use with, or in conjunction with, an instruction execution system, apparatus, or device.

[0092] More specific examples of computer devices (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer device may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0093] Among them, the memory may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside the data processing device. In a specific embodiment, the memory is a non-volatile memory. In a specific embodiment, the memory includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0094] Example 4

[0095] The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a terminal, which includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the financial loan risk prediction method based on big data of the above embodiment.

[0096] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0097] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0098] Under the premise that no conflict occurs, those skilled in the art may freely combine and superimpose the above-mentioned additional technical features.

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0100] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0101] The above-described embodiments merely represent several implementation methods of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A white balance adjustment method, characterized in that: The method comprises: Dividing the original image into a plurality of regions, and determining corresponding correction points in each of the regions; Converting each color channel of the correction point to a YCrCb space, and calculating the cumulative values ​​of the Cb component and the Cr component corresponding to each correction point based on the constraint conditions, calculating the gain value of the color channel of the correction point based on the cumulative values ​​of the Cb component and the Cr component, and performing gain adjustment on each correction point based on the gain value to obtain a target correction point; The pixels in each of the areas are moved closer to the corresponding target correction point to perform white balance adjustment.

2. The white balance adjustment method according to claim 1, wherein: The method further comprises: A YCbCr color coordinate system is established, with the intersection of two adjacent edges on the original image as the origin, the two edges starting from the origin as the Cb axis and the Cr axis respectively, and the plane passing through the origin and perpendicular to the Cb axis and the Cr axis as the Y axis.

3. The white balance adjustment method according to claim 1, wherein: The conversion expressions related to the step of converting each color channel of the correction point to the YCrCb space are as follows: Here, R, G, and B are component values ​​of different channels of the correction point, and Y, Cb, and Cr are the Y axis, Cb axis, and Cr axis of the YCbCr color coordinate system, respectively.

4. The white balance adjustment method according to claim 1, wherein: The step of calculating the gain value of the color channel of the correction point based on the accumulated value of the Cb component and the Cr component includes: Comparing the accumulated value of the Cb component and the accumulated value of the Cr component with corresponding preset thresholds, respectively, and calculating the mean value of the Cb component and the mean value of the Cr component based on the corresponding comparison results; The gain value of the color channel of the correction point is calculated based on the average value of the Cb component and the average value of the Cr component.

5. The white balance adjustment method according to claim 1, wherein: The expression for performing gain adjustment on each correction point based on the gain value is as follows: r_o=r_i+k*mul_r g_o=g_i b_o=b_i+k*mul_b Among them, r_o, g_o and b_o are the output values ​​of the R, G and B color channels of the correction point respectively, r_i, g_i and b_i are the values ​​of the color channels of the previous frame respectively, k is the rate factor, mul_r and mul_b are the gain values ​​of the G channel and B channel respectively.

6. The white balance adjustment method according to claim 1, wherein: Before the step of moving the pixel points in each of the regions closer to the corresponding target correction point, the method further includes: The position of each pixel on the original image is obtained through the CL5 lens, and the area to which each pixel belongs is determined based on the position of the pixel.

7. A white balance adjustment system, configured to implement the white balance adjustment method according to any one of claims 1 to 6, the system comprising: A division module, configured to divide the original image into a plurality of regions and determine a corresponding correction point in each region; an adjustment module, configured to convert each color channel of the correction point into a YCrCb space, and calculate, based on a constraint condition, a cumulative value of a Cb component and a Cr component corresponding to each correction point, respectively, calculate a gain value of the color channel of the correction point based on the cumulative value of the Cb component and the Cr component, and perform gain adjustment on each correction point based on the gain value to obtain a target correction point; The moving-together module is used to move the pixel points in each of the areas toward the corresponding target correction point to perform white balance adjustment.

8. The white balance adjustment system according to claim 7, wherein: The system further comprises: A YCbCr color coordinate system is established, with the intersection of two adjacent edges on the original image as the origin, the two edges starting from the origin as the Cb axis and the Cr axis respectively, and the plane passing through the origin and perpendicular to the Cb axis and the Cr axis as the Y axis.

9. The white balance adjustment system according to claim 7, wherein: The conversion expressions related to the step of converting each color channel of the correction point to the YCrCb space are as follows: Here, R, G, and B are component values ​​of different channels of the correction point, and Y, Cb, and Cr are the Y axis, Cb axis, and Cr axis of the YCbCr color coordinate system, respectively.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the white balance adjustment method according to any one of claims 1 to 6 is implemented.

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